Artificial Intelligence (AI) for Past Performance Evaluation

AI for Past Performance Evaluation

Introduction

The President’s Management Agenda calls for using automation software to improve efficiency of government services. A key focus area is past performance evaluations. Past performance information is relevant information, for future source selection purposes, regarding a contractor’s actions under previously awarded contracts or orders. The Contract Performance Assessment Reporting System (CPARS) is the official source for past performance information. Government-wide acquisition modernization efforts, led by the DHS Office of the Chief Procurement Officer through the Procurement Innovation Lab, seek to determine the extent to which artificial intelligence (AI) can help contracting officers make more efficient and effective use of CPARS data by rapidly identifying relevant records.

CORMAC was selected as one of several contractors to build a prototype. We did so leveraging our Software as a Service (SaaS) product, CORMAC Envisioning and Prediction Enhancing System (CREPES). CREPES serves as a next-generation decision support tool, applying the powers of machine learning (ML) with natural language processing (NLP) to streamline the time-consuming process of past performance evaluation for the federal acquisition workforce.

The CREPES product applied ML algorithms, developed based on a human-scored baseline, to data derived from text analytics. It did so using NLP, which compared an active solicitation to offerors’ CPARS records. A meta-analysis was done to combine the statistical results from both factorized data and free-form narrative. Together, this generated a score describing how relevant past projects were to the current requirement, as the government considers success in those contracts to correlate strongly with positive future performance.

Business Benefits/Outcomes

The initial prototype created a pathway to significantly reduce the administrative workload on contracting officers conducting past performance evaluations while increasing the quality of the outcome. This self-sustaining solution will enable the government acquisition workforce to shift its attention to higher-value work and focus more on activities that prioritize the mission.

Innovations to Bring Business Value

Augmented Analytics: Combining the powers of Machine Learning, NLP, HCD and Data Visualization.

The CREPES product capitalizes on the advancements in ML and NLP to access a whole new world of possibilities. We used various NLP and ML libraries for text analytics and sentimental analysis to automate labor-intensive past performance evaluation. CREPES blends information engineering with textual data using neural networks to summarize and predict a vendor who is likely to succeed when awarded a contract. It utilizes the capabilities of In-memory computing on AWS to scan through thousands of performance records and recommend a vendor based on different criteria, explaining the logic behind its reasoning. CREPES provides an unbiased list of contractors ranked by performance, rather than relying on human intuition to estimate which contractor is most likely to perform well. Agile scrum methodology brought cadence and predictability to the three-month development process. Human-centered design (HCD) techniques were used to create the optimal end-user experience. Additionally, CORMAC used data visualization techniques to display results more intuitively via vector graphics and reports.

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